class_docstr: Generate latent parameters for sentence data. 
code: def forward(self, x):
      e = self.enc(self.embedding(x.long()).unsqueeze(1))
      mu, logvar = self.c1(e).squeeze(), self.c2(e).squeeze()
      return mu, F.softplus(logvar) + Constants.eta
  
imports: import json
  import os
  import numpy
  import torch
  import torch.distributions
  import torch.nn
  import torch.nn.functional
  import torch.utils.data
  from torch.utils.data import DataLoader
  from datasets import CUBSentences
  from utils import Constants, FakeCategorical
  from .vae import VAE
label_desc: Use image node_id 2 for calling the Enc obj's underlying member method code with example usage: obj.forward(x) and returns: mu,  unknown